Wild Bootstrap Inference for Event Studies — bootstrap

Computes bootstrap p-values for AAR and CAAR test statistics using the wild bootstrap approach. Per-firm random weights preserve the cross-sectional dependence structure while randomizing the sign of abnormal returns under the null hypothesis.

Usage

bootstrap_test(
  task,
  n_boot = 999L,
  weight_type = "rademacher",
  statistic = "both",
  group = NULL,
  seed = NULL
)

Arguments

task: A fitted EventStudyTask with abnormal returns computed.
n_boot: Number of bootstrap replications. Default 999.
weight_type: Type of bootstrap weights: "rademacher" (default, +1/-1 with equal probability) or "mammen" (Mammen two-point distribution).
statistic: Which statistic to bootstrap: "aar", "caar", or "both" (default).
group: Optional group name to filter.
seed: Optional seed for reproducibility.

Value

A tibble with columns: relative_index, observed_aar, observed_caar, boot_p_aar, boot_p_caar.